• Journal of Korean Association for Spatial Structures
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• v.9 no.1
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• pp.23-26
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• 2009

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.501-512
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• 2018

We propose Sequential Patient Recruitment Monitoring (SPRM), a new monitoring procedure for patient recruitment in a clinical trial. Based on the sequential probability ratio test using improved stopping boundaries by Woodroofe, the method allows for continuous monitoring of the rate of enrollment. It gives an early warning when the recruitment is unlikely to achieve the target enrollment. The packet data approach combined with the Central Limit Theorem makes the method robust to the distribution of the recruitment entry pattern. A straightforward application of the counting process framework can be used to estimate the probability to achieve the target enrollment under the assumption that the current trend continues. The required extension of the recruitment period can also be derived for a given confidence level. SPRM is a new, continuous patient recruitment monitoring tool that provides an opportunity for corrective action in a timely manner. It is suitable for the modern, centralized data management environment and requires minimal effort to maintain. We illustrate this method using real data from two well-known, multicenter, phase III clinical trials.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.7-14
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• 2014

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1313-1320
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• 2012

Condition monitoring of the reciprocating compressor is important task. As a traditional method, health monitoring system of refrigerator depends on decision of a skilled person based on his experience. However, the skilled person cannot monitor all the compressors completely. If a sampled compressor is faulty, thousands of compressors manufactured at that place are regarded as faulty compressors. Therefore it is necessary to monitor all compressors in the production line. In the paper real time health monitoring system is developed based on high order time frequency method and artificial neural network. The system is installed in the mass production line. The result of the application has been very successful, and currently the system is working very well on the production line.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.8
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• pp.553-558
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• 2015

This paper aims at making a dedicated vibration monitoring and diagnosis framework for 3MW WTG(wind turbine generator). Within the scope of the research, vibration data of WTG drive train are used and WTG operating conditions are involved for dividing the vibration data class which included transient and steady state vibration signals. We separate two health detections which are CHD(continuous health detection) and EHD(event health detection). CHD has function of early detection and continuous monitoring. EHD makes the use of finding vibration values of fault components effectively by spectrum matrix subsystem. We proposed framework and showed application for 3MW WTG in a practical point of view.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Journal of the Korean Society for Nondestructive Testing
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• v.26 no.6
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• pp.373-379
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• 2006

Vibration-based structural health monitoring is one of non-destructive evaluation (NDE) techniques for civil infrastructures. This paper presents a novel fiber optic accelerometer system to monitor civil engineering structures and a successful application of the novel sensor system for measuring ambient vibration of a real building structure. This sensor system integrates the Moire fringe phenomenon with fiber optics to achieve accurate and reliable measurements. The sensor system is immune to electromagnetic (EM) interference making it suitable for difficult applications in such environments involving strong EM fields, electrical spark-induced explosion risks, and cabling problems, prohibiting the use of conventional electromagnetic accelerometers. A prototype sensor system has been developed, together with a signal processing software. The experimental studies demonstrated the high-performance of the fiber optic sensor system. Especially, the sensor was successfully used for monitoring a real building on UCI (University of California Irvine, USA).

• Structural Engineering and Mechanics
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• v.18 no.5
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• pp.609-626
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• 2004

The Land Transport Authority of Singapore has a continuing program of highway bridge upgrading, to refurbish and strengthen bridges to allow for increasing vehicle traffic and increasing axle loads. One subject of this program has been a short span bridge taking a busy highway across a coastal inlet near a major port facility. Experiment-based structural assessments of the bridge were conducted before and after upgrading works including strengthening. Each assessment exercise comprised two separate components; a strain and acceleration monitoring exercise lasting approximately one month, and a full-scale dynamic test carried out in a single day. This paper reports the application of extreme value statistics to estimate bridge live loads using strain measurements.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.273-276
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• 2006

In this paper the recent research and application activities on smart structure technologies for civil infra structures in Korea are briefly introduced. The developments of structural health monitoring systems and effective retrofit/maintenance methodologies for infra structures have become active in Korea since the middle of 1990's, as the number of the deteriorated infra structures, mostly built on the rapidly industrialized period of 1970's, has increased very rapidly. Discussions are made on smart sensors, non destructive technologies, monitoring and assessment methods and systems for civil infra structures.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.21-32
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• 2003

Developments and applications of the structural health monitoring (SHM) systems have become active particularity for long-span bridges in Korea. They are composed of sensors, data acquisition system, data transmission system, information processing, damage assessment, and information management. In this paper, current status of research and application activities on SHM systems for civil infra-structures in Korea are briefly introduced by 4 parts: (1) current status of bridge monitoring systems on existing and newly constructed bridges, (2) research and development activities on smart sensors such as optical fiber sensors and piezo-electric sensors, (3) structural damage detection methods using measured data, and (4) a test road project for pavement design verification and enhancement by the Korea Highway Corporation. Finally the R＆D activities of a new engineering research center entitled Smart Infra-Structure Technology Center at Korea Advanced Institute of Science and Technology are also briefly described.